Problem Statement Advanced Technologies for IoT Applications Results References Future work Title: Film2Vec – A Feature-based Film Distributed Representation for Rating Prediction Abstr
Trang 1Problem Statement
Advanced Technologies for IoT Applications
Results
References
Future work
Title: Film2Vec – A Feature-based Film Distributed Representation for Rating Prediction
Abstract: Approaches for film recommendation systems usually exploit explicit descriptive features to compute ratings In this paper, we suggest a different approach – to rate films via their related neighbors computed via
distributed representation of movies Specifically, we present Film2Vec, a distributed representation learning
for films adapted from the distributed hypothesis from linguistics We implement our proposed idea using
TensorFlow, a Google’s Deep Neural Networks software The experimental results on Movielens dataset show
that Film2Vec can effectively reduce root mean square error (RMSE) in movie recommendation task, suggesting yet another beneficial application of deep learning
Contributions
Recommendation systems
Recommend
Many works use rating information
Film2Vec – Representing Films as Vectors
Pre-processing
Film2Vec
Film1 A1 D120 G19 T18
Film2 A13 D14 G156 T17
Film3 A12 D23 G43 T65
…
Filmn A45 D2 G4 T1
Film vectors
HetRec 2011
Film descriptions
Few works use context of recommendation system
0.7 0.75 0.8 0.85 0.9 0.95
1 1.05 1.1
F2V-TA F2V-TDGA CF CA ARR LLS IMBRF
Context of film:
• Title
• Actors - A
• Tags - T
• Genres - G
• Directors - D
Best F2V-TDGA
• Use other information of film such as
countries, location and plot.
• Apply to other areas such as books,
services, and papers.
[1] Baroni et al “Don’t count, predict! A systematic comparison of context-counting vs
context-predicting semantic vectors”, ACL, 2014.
[2] Bothos et al “Information market based recommender systems fusion”, HetRec, 2011 [3] Mikolov et al “Efficient Estimation of Word Representations in Vector Space”, ICLR,
2013